Use of an entropy measure in supervised learning

Summary form only given, as follows. The author derives a supervised backpropagation learning rule from the log likelihood or entropy of network output. The training performance of this learning rule is compared to the conventional squared error measure learning rule. The Fischer Iris data set is employed for the network training. A modest improvement in performance was observed with the entropy measure over the squared error measure.<>